Skip to Main Content
This paper presents the endpoint detection approaches specifically for an isolated word uses Malay spoken speeches from Malaysian Parliamentary session. Currently, there are 34,466 vocabularies of utterances in the database collection and for the purpose of this study; the vocabulary is limited to 25 words which are most frequently spoken selected from ten speakers. Endpoint detection, which aims to distinguish the speech and non-speech segments of digital speech signal, is considered as one of the key preprocessing steps in speech recognition system. Proper estimation of the start and end of the speech (versus silence or background noise) avoids the waste of speech recognition evaluations on preceding or ensuing silence. In this study, the endpoint detection and speech segmentation task is achieved by using the three different algorithms, namely combination between Short-time Energy (STE) and Zero Crossing Rate (ZCR) measures, frame-based Teager's Energy (FTE), and Energy-Entropy feature (EEF). Three experiments were conducted separately to investigate the overall recognition rate obtained with a Discrete-Hidden Markov Model (DHMM) classifier approach on the testing data set that consists of 1250 utterances. The results show that EEF algorithm performs quite satisfactory and acceptable where average recognition rate is 80.76% if compared with other two algorithms. Each of the algorithms have the advantages and disadvantages and there are still misdetection of word boundaries for the words with weak fricative, plosive and nasal sounds and not robust enough to implement in Malaysian Parliamentary speech data. However, improvement is still possible to increase the performance of these algorithms.